Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Mlscp
Parse and generate MLSCP (Micro LLM Swarm Communication Protocol) commands. Use when communicating with other agents efficiently, parsing compressed commands, or generating token-efficient instructions. Reduces token usage by 70-80% compared to natural language.
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Parse and generate MLSCP (Micro LLM Swarm Communication Protocol) commands. Use when communicating with other agents efficiently, parsing compressed commands, or generating token-efficient instructions. Reduces token usage by 70-80% compared to natural language.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- sirkrouph-dev
- Source page
- View original listing
- Canonical URL
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Documentation
MLSCP Skill
MLSCP (Micro LLM Swarm Communication Protocol) is a token-efficient command language for agent-to-agent communication. This skill lets you parse, validate, and generate MLSCP commands without any LLM overhead.
Why Use MLSCP?
Natural LanguageMLSCPSavings"Please modify the file src/chain_orchestrator.py by adding retry logic at line 47"F+ s/co > ln47 + 'retry logic'~75%"Read the contents of utils/file_manager.py from lines 10 to 50"F? u/fm > ln10-50~80%"Delete the variable 'temp_cache' from config.py"V- c/c > 'temp_cache'~70%
Parse a Command
./scripts/mlscp.sh parse "F+ s/co > ln47 + 'retry logic'"
Validate Syntax
./scripts/mlscp.sh validate "F+ s/co > ln47 + 'retry logic'"
Generate Vocabulary
./scripts/mlscp.sh vocab /path/to/project
Compress Natural Language
./scripts/mlscp.sh compress "Add error handling to the main function in app.py"
Operations
CodeMeaningExampleF+File add/insertF+ s/app > ln10 + 'new code'F~File modifyF~ s/app > ln10-20 ~ 'updated code'F-File deleteF- s/app > ln10-15F?File query/readF? s/app > ln1-100V+Variable addV+ s/app + 'new_var = 42'V~Variable modifyV~ s/app > 'old_var' ~ 'new_value'V-Variable deleteV- s/app > 'temp_var'V?Variable queryV? s/app > 'config_*'
Location Specifiers
ln47 - Single line ln10-50 - Line range fn:main - Function name cls:MyClass - Class name
Context Blocks
CTX{"intent":"resilience","priority":"high","confidence":0.9}
Scripts
mlscp.sh - Main CLI tool vocab.py - Vocabulary generator (Python)
With Other Agents
When receiving commands from MLSCP-enabled agents: ./scripts/mlscp.sh parse "$INCOMING_COMMAND"
Sending Commands
Generate compact commands for other agents: ./scripts/mlscp.sh compress "Your natural language instruction"
API (Python)
from mlscp import parse, MLSCPParser # Quick parse cmd = parse("F+ s/co > ln47 + 'retry logic'") print(cmd.operation) # OperationType.FILE_ADD print(cmd.target) # "s/co" # With vocabulary parser = MLSCPParser(vocab_lookup) cmd = parser.parse("F+ s/co > ln47 + 'code'") full_path = vocab_lookup.get("s/co") # "src/chain_orchestrator.py"
Resources
GitHub: https://github.com/sirkrouph-dev/mlcp Grammar Spec: See references/grammar.abnf Protocol Definition: See references/protocol.md
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md